Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images

1. Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Blood vessel segmentation methodologies in retinal images–a survey. Computer Methods and Programs in Biomedicine 108(1), 407–433 (2012)

   Article  Google Scholar 

2. Miri, M.S., Mahloojifar, A.: Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. IEEE T-BME 58(5), 1183–1192 (2011)

   Article  Google Scholar 

3. Kanski, J.J., Bowling, B.: Synopsis of Clinical Ophthalmology. Saunders Limited (2012)

   Google Scholar 

4. Li, Y., Gregori, G., Knighton, R.W., Lujan, B.J., Rosenfeld, P.J.: Registration of OCT fundus images with color fundus photographs based on blood vessel ridges. Optics Express 19(1), 7 (2011)

   Article  Google Scholar 

5. Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE T-MI 26(10), 1357–1365 (2007)

   Article  Google Scholar 

6. Becker, C., Rigamonti, R., Lepetit, V., Fua, P.: Supervised feature learning for curvilinear structure segmentation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 526–533. Springer, Heidelberg (2013)

   Chapter  Google Scholar 

7. Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE T-MI 25(9) (2006)

   Google Scholar 

8. Martinez-Perez, M.E., Hughes, A.D., Thom, S.A., Bharath, A.A., Parker, K.H.: Segmentation of blood vessels from red-free and fluorescein retinal images. Medical Image Analysis 11(1), 47–61 (2007)

   Article  Google Scholar 

9. Zhang, B., Zhang, L., Zhang, L., Karray, F.: Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Computers in Biology and Medicine 40(4), 438–445 (2010)

   Article  Google Scholar 

10. Nguyen, U.T., Bhuiyan, A., Park, L.A., Ramamohanarao, K.: An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognition (2012)

    Google Scholar 

11. Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Ensemble classification system applied for retinal vessel segmentation on child images containing various vessel profiles. Image Analysis and Recognition (2012)

    Google Scholar 

12. Vlachos, M., Dermatas, E.: Multi-scale retinal vessel segmentation using line tracking. Computerized Medical Imaging and Graphics 34(3), 213–227 (2010)

    Article  Google Scholar 

13. Li, S.Z.: Markov Random Field Modeling in Image Analysis, 3rd edn. Springer (2009)

    Google Scholar 

14. Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: NIPS (2012)

    Google Scholar 

15. Joachims, T., Finley, T., Yu, C.N.J.: Cutting-plane training of structural SVMs. Machine Learning 77(1), 27–59 (2009)

    Article  MATH  Google Scholar 

16. Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE T-MI 23(4), 501–509 (2004)

    Article  Google Scholar 

17. Schölkopf, B.: Support Vector Learning. PhD thesis, Oldenbourg Verlag, Munich (1997)

    Google Scholar 

18. Soares, J.V., Leandro, J.J., Cesar, R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-d Gabor wavelet and supervised classification. IEEE T-MI 25(9) (2006)

    Google Scholar 

19. Al-Rawi, M., Qutaishat, M., Arrar, M.: An improved matched filter for blood vessel detection of digital retinal images. Computers in Biology and Medicine 37(2), 262–267 (2007)

    Article  Google Scholar 

20. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

21. Marín, D., Aquino, A., Gegúndez-Arias, M.E., Bravo, J.M.: A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE T-MI 30(1), 146–158 (2011)

    Article  Google Scholar 

22. Sinthanayothin, C., Boyce, J.F., Cook, H.L., Williamson, T.H.: Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. British Journal of Ophthalmology 83(8), 902–910 (1999)

    Article  Google Scholar 

23. Saleh, M.D., Eswaran, C.: An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding. Computer Methods in Biomechanics and Biomedical Engineering 15(5), 517–525 (2012)

    Article  Google Scholar 

24. Zana, F., Klein, J.-C.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE TIP 10(7), 1010–1019 (2001)

    MATH  Google Scholar 

25. You, X., Peng, Q., Yuan, Y., Cheung, Y.-M., Lei, J.: Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognition 44(10) (2011)

    Google Scholar 

26. Palomera-Pérez, M.A., Martinez-Perez, M.E., Benítez-Pérez, H., Ortega-Arjona, J.L.: Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection. IEEE T-ITB 14(2), 500–506 (2010)

    Google Scholar 

27. Al-Diri, B., Hunter, A., Steel, D.: An active contour model for segmenting and measuring retinal vessels. IEEE T-MI 28(9), 1488–1497 (2009)

    Article  Google Scholar 

28. Espona, L., Carreira, M.J., Penedo, M.G., Ortega, M.: Retinal vessel tree segmentation using a deformable contour model. In: ICPR (2008)

    Google Scholar 

29. Espona, L., Carreira, M.J., Ortega, M., Penedo, M.G.: A snake for retinal vessel segmentation. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4478, pp. 178–185. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

Download references